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A Step-Ramp-Step Treadmill Protocol Predicts Exercise Intensity Domain-Specific vo2 Responses In Running

2023· article· en· W4387061758 on OpenAlexaff
Robin Faricier, Lorenzo Micheli, Nasimi A. Guluzade, Juan M. Murias, Daniel A. Keir

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of CalgaryWestern University
Fundersnot available
KeywordsRespiratory compensationTreadmillExercise prescriptionLactate thresholdExercise intensityIntensity (physics)Ventilatory thresholdIncremental exerciseMathematicsCyclingVO2 maxCardiologySimulationMedicineHeart ratePhysical therapyBlood lactateAnaerobic exerciseInternal medicinePhysicsComputer scienceBlood pressure

Abstract

fetched live from OpenAlex

The “step-ramp-step” (SRS) protocol improves domain-based exercise intensity prescription in cycling by adjusting the ramp-exercise oxygen uptake (V̇O2) versus work-rate relationship for V̇O2 kinetics and muscle-lung circulatory delay. PURPOSE: This study investigated whether similar corrections are required in running by comparing the accuracy of a treadmill-based SRS protocol with the “classic” (unadjusted) linear approach to predict domain-specific V̇O2 responses. METHODS: Five healthy males (age: 26 ± 9 years) performed a ramp-incremental test (0.6 km·h-1·min-1) from 6 km·h-1 to task failure followed by two constant-speed bouts within the moderate- (MOD - below estimated lactate threshold; LT) and heavy-intensity domains (HVY - between LT and respiratory compensation point; RCP). The V̇O2 at LT and RCP were visually identified by plotting standard breath-by-breath ventilatory and gas exchanges measurements against ramp V̇O2. Then, the corresponding speed at LT and RCP, derived from the unadjusted linear V̇O2-speed relationship from the ramp exercise (“classic” linear approach), were used to establish two domain-specific constant-speed bouts at 50% between: i) baseline and LT (ΔMOD); and ii) LT and RCP (ΔHVY). Constant-speed bouts were performed on separate days, and “measured” end-exercise V̇O2 was compared to “predicted” V̇O2 using a: i) “SRS-corrected” (where MOD and HVY bouts were used to adjust the ramp V̇O2-speed relationship); and ii) unadjusted V̇O2-speed relationship from ramp exercise. RESULTS: The treadmill speeds for ΔMOD, and ΔHVY were 8.1 ± 1.2 and 11.4 ± 2.0 km·h-1, respectively, eliciting end-exercise V̇O2 responses of 2.19 ± 0.28 and 2.81 ± 0.26 L·min-1. The measured end-exercise V̇O2 were not different compared to SRS-predicted V̇O2 at ΔMOD (mean difference: -0.04 ± 0.19 L·min-1; p = 0.70) and ΔHVY (0.08 ± 0.30 L·min-1; p = 0.57). Compared to the classic-predicted V̇O2, the measured end-exercise V̇O2 was not different from predicted for ΔMOD (0.15 ± 0.15 L·min-1; p = 0.09) but was higher for ΔHVY (0.19 ± 0.14 L·min-1; p = 0.04). CONCLUSION: In healthy individuals, the SRS protocol provides more accurate V̇O2 response predictions than the “classic” linear approach and potentially represents a better alternative for domain-based exercise-intensity prescriptions. Supported by NSERC

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.309
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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